Given (A, b) in view, return (sum(A,axis=0), sum(b, axis=0))

\returns a const pointer to the array of values. * This function is aimed at interoperability with other libraries. * \sa innerIndexPtr(),

Check that inverted solution matches direct solve for all vars and duals.

Solve with both SciPy and COO backends, return both optimal values.

Addition for dict-based tensors.

Returns a list of the relevant attributes present among the variables.

* Get the number of columns of the input matrix. */

\returns true if \a U (full or thin) is asked for in this SVD decomposition */

Create an empty CooTensor with given dimensions.

\returns the tolerance error reached during the last solve. * It is a close approximation of the true relative residual error |Ax-b|/|b|. */

Does the problem satisfy disciplined nonlinear programming (DNLP) rules?

Whether the problem has integer or boolean variables.

Is the expression all zero?

\internal */

Multiply view with constant data from the left. When the lhs is parametrized, multiply each slice of the tensor with the sing

Factory function for a mathematical p-norm. Parameters ---------- p : numeric type or string The type of norm to construct; set th

Select 'rows' from tensor.

\internal * \sa insertBack, insertBackByOuterInner */

TLDR: Use alternate formulation for trace(A@B) for more efficient computation. trace(A@B) normally is O(n^3) because of the A@B operation.

* Return the array of nonzero values packed by column * * The size is nnz */

Reshapes a vector into an upper triangular matrix in row-major order. The strict argument specifies whether an upper or a strict upper triangular

Left-multiply each variable tensor in the view by per-parameter matrices.

Decompose a single ||x|| <= t constraint into tree of exactly 3D cones. Parameters ---------- t_expr : Expression The scalar boun

Convert a dual value to flat array format. Dual values can come in two formats: - Flat array: [lambda, mu1, mu2, ...] (from solver) - Spl

Construct an initial point for variables without a user-specified value. Uses get_bounds() which incorporates sign attributes (nonneg, nonpos, et

Convert sparse matrix or dense array to CooTensor.

\internal Frees memory allocated with aligned_malloc. */

Accessor method for atoms. Returns ------- list of :class:`~cvxpy.atoms.Atom` A list of the atom types in the pro

Main function called from canonInterface. Given a list of LinOp trees, each representing a constraint (or the objective), get the

Get the permutation that was applied to columns of A

Concatenates the row, col, parameter_offset, and data fields of a list of TensorRepresentations.

* Compute the symbolic and numeric factorization of the input sparse matrix. * The input matrix should be in column-major storage. */

Convert an arbitrary value into a matrix of type self.target_matrix. Args: value: The constant to be converted. conve

Evaluate constraints only. Returns constraint_values array.

Matrix multiplication of two CooTensors. lhs has shape (param_size_lhs, m, k) per slice rhs has shape (param_size_rhs, k, n) per slice

Diagonal vector to matrix. Given (A, b) with n rows in view, add rows of zeros such that the original rows now correspond to the diag

Replace symbolic last/end "keywords" by their true runtime value

We expand the matrix A to B = [[Re(A), -Im(A)], [Im(A), Re(A)]]. The resulting matrix has special structure if A is Hermitian. Specifica